The HHIP-AS1 lncRNA promotes tumorigenicity through stabilization of dynein complex 1 in human SHH-driven tumors

Most lncRNAs display species-specific expression patterns suggesting that animal models of cancer may only incompletely recapitulate the regulatory crosstalk between lncRNAs and oncogenic pathways in humans. Among these pathways, Sonic Hedgehog (SHH) signaling is aberrantly activated in several human cancer entities. We unravel that aberrant expression of the primate-specific lncRNA HedgeHog Interacting Protein-AntiSense 1 (HHIP-AS1) is a hallmark of SHH-driven tumors including medulloblastoma and atypical teratoid/rhabdoid tumors. HHIP-AS1 is actively transcribed from a bidirectional promoter shared with SHH regulator HHIP. Knockdown of HHIP-AS1 induces mitotic spindle deregulation impairing tumorigenicity in vitro and in vivo. Mechanistically, HHIP-AS1 binds directly to the mRNA of cytoplasmic dynein 1 intermediate chain 2 (DYNC1I2) and attenuates its degradation by hsa-miR-425-5p. We uncover that neither HHIP-AS1 nor the corresponding regulatory element in DYNC1I2 are evolutionary conserved in mice. Taken together, we discover an lncRNA-mediated mechanism that enables the pro-mitotic effects of SHH pathway activation in human tumors.

S onic hedgehog (SHH) signaling plays a pivotal role in promoting oncogenesis, tumor growth and progression 1 . It is aberrantly activated in various common cancers in adults, including basal cell carcinoma (BCC) 2 , but also in pediatric neoplasms, including rhabdomyosarcoma 3 and brain tumors such as medulloblastoma (MB) and atypical teratoid/rhabdoid tumors (ATRT) [4][5][6][7][8] . Pediatric brain tumors, like MB and ATRT, are the most common solid malignancies of childhood and the leading cause of cancer-related death in children 9 . Both entities are highly heterogeneous and can be segregated into distinct subgroups by virtue of their divergent molecular characteristics. Notably, such classification, which is primarily based on intergroup differences detected at multi-omics level, is of clinical utility, as it correlates with specific and distinct clinicopathological and prognostic patterns [10][11][12] . Specifically, MB comprises four main subgroups, designated as Wingless (WNT), SHH, Group 3 and Group 4 [13][14][15][16] . WNT and SHH MBs are named according to the signaling pathways that drive their formation and progression 17,18 . Conversely, the other two MB subgroups are less defined in their molecular etiology 19,20 , although Group 3 MB recurrently displays MYC amplification and/or overexpression [21][22][23][24] , while Group 4 MB commonly show activation of receptor tyrosine kinase signaling through aberrant expression of ERBB4 and the phosphorylated form of the tyrosine-protein kinase SRC 25 . In the case of ATRT, three subgroups have been defined, namely tyrosinase (TYR), MYC and SHH, according to the distinctive overexpression of TYR or MYC genes, or the activation of SHH signaling 4,6,26 .
Targeting the SHH signaling to extinguish its mitogenic effects in SHH MB has shown efficacy in pre-clinical animal models and in humans 27 . However, the clinical use of SHH inhibitors, such as the G protein-coupled receptor smoothened (SMO) antagonist Vismodegib (GDC-0449), is limited due to toxicity or emergence of drug resistance in children with SHH MB to date 28,29 . While mutation and altered expression of several protein-coding genes are well-established drivers of SHH-dependent tumorigenesis, the precise impact of the non-protein coding genome remains largely unexplored. In particular, investigations of the specific role of long non-coding RNAs (lncRNAs) in SHH-driven malignancies are just emerging as an important research field 30,31 . Most research efforts have so far focused on well-defined and comprehensive murine models of SHH-driven tumors 32,33 . However, these models fail to offer a faithful representation of the human lncRNAs landscape, because lncRNAs show only poor conservation across species 34 . Nevertheless, lncRNAs are known to play essential roles in all aspects of cell biology [35][36][37] and the regulatory mechanisms orchestrated by lncRNAs are diverse including direct binding to chromatin 38 , proteins 39 or regulating microRNAs 40 . Conversely, the role of non-coding RNAs in the pathogenesis and disease stratification of ATRT has not been elucidated so far, and non-coding RNA classes also remain poorly characterized in MB.
Here, we explore the role of lncRNAs specifically in SHH-driven tumors with the aim to decipher the impact in SHH signaling regulation and function in these tumors. We show that aberrant expression of the lncRNA Hedgehog Interacting Protein Antisense 1 (HHIP-AS1) is a hallmark of human SHH-driven tumors including MB and ATRT. HHIP-AS1 is actively transcribed from a SHHresponsive bidirectional promoter shared with the SHH signaling intermediate HHIP. HHIP-AS1 knockdown leads to reduced tumor growth in SHH-driven tumors in vitro and in vivo by decreasing cell proliferation and inducing mitotic spindle deregulation. Mechanistically, we show that HHIP-AS1 binds to and stabilizes the mRNA of cytoplasmic dynein 1 intermediate chain 2 (DYNC1I2), a component of the cytoplasmic dynein 1 complex, by preventing hsa-miR-425-5p-mediated degradation. We uncover that neither HHIP-AS1 nor the corresponding regulatory element in DYNC1I2 are evolutionary conserved in mice demonstrating the power of crossentity transcriptome-wide comparisons to identify regulatory lncRNA circuitries in human cancer. In all, we discover a lncRNAdependent blockage of endogenous RNA mechanism as an additional layer of epigenetic regulation mediated by HHIP-AS1 as a human-specific target gene in SHH-dependent cell progression.

Results
Overexpression of the long non-coding RNA HHIP-AS1 is a hallmark of human SHH-driven tumors. To discover global SHH-dependent gene expression patterns in cancer, we first determined differentially expressed transcripts in RNA sequencing data by comparing SHH MB (n = 58) to non SHH MB subgroup samples (n = 164) (Platform, R2 41 ). Our approach confirmed several known protein-coding SHH mediators including GLI1, GLI2, HHIP and Atonal BHLH Transcription Factor 1, and revealed lncRNAs deregulated in SHH MB (Fig. 1a, Supplementary Data file 1). Among these candidates, we identified HHIP-AS1 as the most specifically overexpressed lncRNA in SHH MB (Fig. 1a, Supplementary Data file 1) without proteincoding potential (Fig. S1a). We next investigated whether HHIP-AS1 was similarly overexpressed in other tumor entities with aberrant activation of the SHH signaling. Comparative expression analysis confirmed specific HHIP-AS1 overexpression in SHHdriven entities including ATRT (Fig. 1b), cutaneous BCC (Fig. S1b) and rhabdomyosarcoma (Fig. S1c) compared to normal and cancerous control tissues. Consistent with its overexpression, we next revealed active transcription of HHIP-AS1 from a hypomethylated promoter shared with HHIP, a well-established regulator of the SHH pathway and whose genomic localization shows a head-to-head orientation with the HHIP-AS1 locus, exclusively in SHH MB using both H3K27ac ChIP-sequencing ( Fig. 1c) and high-resolution DNA methylation data ( Fig. 1d and S1d). Transcriptome-wide correlation analyses consistently revealed that HHIP and HHIP-AS1 are co-expressed in MB (n = 167, r = 0.990, p < 0.001, Fig. S1e) and in ATRT (n = 49, r = 0.836, p < 0.001, Fig. S1f) cohorts. Furthermore, we could reveal that the two genes exhibited remarkably high and significant correlative co-expression scores in multiple expression datasets, including other cancer entities and control tissues (n = 39,090; p < 0.001, Fig. 1e). These observations prompted us to hypothesize that HHIP-AS1 and HHIP may indeed share a unique bidirectional promoter. Therefore, we performed a luciferase reporter assay and revealed the existence of forward and reverse activity of the promoter region in vitro (Fig. 1f), suggesting that promoter hypomethylation may equally drive overexpression of both HHIP-AS1 and HHIP transcripts in SHHdriven tumors.
Since HHIP-AS1 was not previously described in murine models of SHH-driven tumors and lncRNAs are only partially conserved through species 34 , we investigated to which extent HHIP-AS1 was conserved across vertebrates. Remarkably, genome-scale comparisons revealed that HHIP-AS1 was only conserved in primates and not in rodents ( Fig. 1g and S1g), explaining why this lncRNA has not been identified in animal models so far.
HHIP-AS1 is functionally required in human SHH-driven brain tumors. Since HHIP-AS1 overexpression is a hallmark in SHH-driven tumors, we investigated whether HHIP-AS1 expression was dependent on SHH signaling in tumor cells. Pharmacological activation of SMO receptor using SAG (SMO agonist) increased HHIP-AS1 expression in two independent tumor cell lines, Daoy and CHLA-266, and one primary cell culture, HHU-ATRT1 (Fig. 2a) as well as in non-tumor cells with SHH activation, 14 Fig. 1 The long non-coding RNA HHIP-AS1 is a hallmark of human SHH-driven tumors. a Long non-coding RNA (lncRNA) expression profiles in SHHdriven medulloblastoma (MB; right side) versus non SHH-driven MB (left side). The volcano plot illustrates the distribution of statistical significance (yaxis) and relative expression level (x-axis) for the lncRNAs profile. The red dot indicates HHIP-AS1 (HedgeHog Interacting Protein-Anti-Sense 1). Statistical analysis was performed using one-way ANOVA with post-hoc Tukey HSD; ***p < 0.001. b Violin plots display the expression level of HHIP-AS1 according to an integrative transcriptomic analysis of 3492 samples from neoplastic brain tissues with SHH activation (SHH MB and atypical teratoid/rhabdoid tumors (ATRT)) or without commonly reported SHH activation (brain tumors) and normal brain without tumor.  Bar graphs of panels a + b are presented as the mean ± SD, panels c-g are presented as the mean ± SEM of at least three independent experiments and corresponding controls were set to 100%. Student's two-sided t-test; ***p < 0.001; **p < 0.01; *p < 0.05. Source data and exact p-values are provided as a "Source Data file".
namely neuronal stem cells (NSC; Fig. S3a). Conversely, inhibition of SMO using cyclopamine (SMO antagonist) significantly reduced HHIP-AS1 in all these cell models ( Fig. 2a and S3a). Activation and inhibition of the SHH pathway were confirmed by quantification of GLI expression level through qRT-PCR ( Fig. 2a and S3a) and immunoblotting ( Fig. S2a-c). GLI proteins are known to function as regulators of the SHH transcriptional response, hence we next examined whether GLI could affect HHIP-AS1 expression. Transient knockdown of GLI1 and GLI2 reduced HHIP-AS1 and HHIP expression in all of the above-mentioned SHH-cancer cell models ( Fig. 2b and S2), while on the other hand overexpression of GLI1 ( Fig. S2d) resulted in increased expression of HHIP and HHIP-AS1 (Fig. S3b), confirming HHIP-AS1 as a target gene of SHH signaling. To evaluate the functional impact of HHIP-AS1, we silenced its expression in vitro using either siRNAs or shRNAs directed against this lncRNA. Strikingly, knockdown of HHIP-AS1 (Fig. S3c) resulted in reduced proliferation, monitored by EdU incorporation (Fig. 2c), as well as reduced clonogenicity (Fig. 2d) of Daoy, CHLA-266 and HHU-ATRT1. In addition, cell viability, assessed through metabolic assay (CellTiter-Glo), was diminished in the SHH MB cell line Daoy and in ATRT cell line CHLA-266 upon HHIP-AS1 silencing (Fig. S3d). We could verify these phenotypic changes after transient HHIP-AS1 knockdown in additional SHH-driven cell models, namely CHLA-04 (ATRT), RH30 (rhabdomyosarcoma), NSC and UI226 (BCC, Fig. S3e). Importantly, we also confirmed that HHIP-AS1 depletion reduced proliferation and viability in primary cultures from two SHH MB patients ( Fig. 2e and f) and impaired proliferation of cultured cells of ICN-MB12 (SHH MB patient derived xenograft model; Fig. 2g).
Notably, overexpression of HHIP-AS1 rescued the reduced proliferation and viability (Fig. S3f), highlighting that HHIP-AS1 is not only aberrantly overexpressed in SHH-driven tumors, but also functionally relevant in these malignancies. Conversely, transfection of HHIP-AS1 siRNAs into non SHH MB cells (HD-MB03 and CHLA-01) with low expression of HHIP-AS1 (Fig. S3g) did not result in reduced proliferation and viability (Fig. S3h), ruling out potential off-target effects and confirming that the functional relevance of HHIP-AS1 is restricted to SHH-driven tumors.
Remarkably, HHIP expression was not affected on mRNA or protein levels upon stable HHIP-AS1 depletion ( Fig. S4a-c). Therefore, we rule out a cis-regulatory effect of HHIP-AS1 on HHIP in our MB and ATRT models.
Identification and functional validation of HHIP-AS1 downstream targets reveals that HHIP-AS1 binds to the mRNA of DYNC1I2. In order to decipher the molecular mechanism controlled by HHIP-AS1, we investigated the impact of HHIP-AS1 knockdown on both transcriptome and proteome in two independent cell models (Daoy and CHLA-266). Interestingly, our integrative proteogenomic analysis identified two candidates that were highly and consistently perturbed upon HHIP-AS1 knockdown in both models, namely hydroxysteroid 17-beta dehydrogenase 10 (HSD17B10) and DYNC1I2 (Fig. 3a, RNA sequencing data: NCBI GenBank #GSE140741 and proteomic data: ProteomeXchange PRIDE database #PXD016550). We decided to focus on DYNC1I2 based on its co-expression with HHIP-AS1 in primary MB samples (r = 0.336; p < 0.001, Fig. 3b), as well as its high expression level specifically in SHH MB subgroup ( Fig. S5a) and its known functions in neurodevelopment and cell cycle progression 42 . HSD17B10 did not show a significant correlation with HHIP-AS1 in primary MB samples (Fig. S5b). We validated by qRT-PCR and immunoblots that HHIP-AS1 depletion reduced the mRNA and protein expression of DYNC1I2 in Daoy, CHLA-266 and HHU-ATRT1 cells ( Fig. S5c and d). To determine the underlying molecular mechanism, we investigated a potential interaction between HHIP-AS1 and DYNC1I2 mRNA. First, we uncovered that both RNAs are colocalized in cells using RNA fluorescence in situ hybridization ( Fig. 3c and S5e) and this co-localization can be enhanced or disrupted via SHH activation or inhibition, respectively (Fig. S5f). Second, we confirmed a direct interaction in each of our three cell models using an RNA-RNA-centric-pulldown probe set directed against HHIP-AS1 RNA, which led to a specific enrichment for DYNC1I2 mRNA compared to negative controls (Fig. 3d). Finally, we computationally derived the sequence of HHIP-AS1 that is predicted to pair with DYNC1I2 mRNA (Supplementary  Table S1), identifying a 24 nucleotides long region (named HHIP-AS1 bind ) able to bind the 5'UTR of DYNC1I2. Furthermore, using bio-layer interferometry, we found that HHIP-AS1 bind could physically interact with DYNC1I2 mRNA (Fig. 3e). In contrast, a negative control sequence of HHIP-AS1 (named HHIP-AS1 neg ), carrying the same GC content and number of nucleotides as HHIP-AS1 bind , but devoid of any in silico pairing potential to DYNC1I2 mRNA, did not show any binding activity (Fig. S5g). Strikingly, we found that in vitro transfection of Daoy or HHU-ATRT1 with HHIP-AS1 bind resulted in extended half-life of DYNC1I2 mRNA ( Fig. 3f and S5h), compared to a control condition where HHIP-AS1 neg was used. All these findings confirm the existence of a functional and direct physical interaction between these two RNAs in vivo and in vitro, which ultimately regulates DYNC1I2 expression levels. In order to elucidate whether the functional interaction between HHIP-AS1 and DYNC1I2 mediates the pro-proliferating phenotype controlled by HHIP-AS1, we evaluated the functional impact of DYNC1I2 depletion in our cell models. Interestingly, transient DYNC1I2 knockdown resulted in reduced proliferation and viability to a similar degree as observed upon HHIP-AS1 depletion ( Fig. S6a and b). More importantly, when we overexpressed DYNC1I2 using CRISPR-Cas9-based activation in HHIP-AS1-transiently depleted cells, both proliferation and viability were restored ( Fig. 3g and h), indicating that HHIP-AS1 exerts its pro-proliferative effects by controlling DYNC1I2 abundance in tumor cells.
The interaction between HHIP-AS1 and DYNC1I2 promotes mitosis. Cytoplasmic dynein-complex 1 has been implicated in various phenotypes including cargo transport on cytoplasmic microtubules and it was recently reported that DYNC1I2 loss disrupts mitotic spindle organization in zebrafish neural progenitor cells 42 . Thus, we hypothesized that HHIP-AS1 loss may cause a similar effect in human tumor cells by reducing DYNC1I2 availability. Indeed, we found that transient knockdown of HHIP-AS1 or DYNC1I2 via siRNAs transfection significantly and consistently altered mitotic spindle organization in cells compared to siRNA control-transfected cells ( Fig. 4a  HHIP-AS1 blocks endogenous hsa-miR-425-5p function to maintain DYNC1I2 level. To further mechanistically elucidate how HHIP-AS1 stabilizes DYNC1I2 mRNA, we evaluated the genetic sequence of RNA-RNA interaction. Our analysis revealed six potential miRNA binding sites (Supplementary Table S2) within the predicted interaction region between HHIP-AS1 and DYNC1I2 mRNA (Fig. 5a), suggesting that HHIP-AS1 binding to DYNC1I2 mRNA may interfere with miRNA-dependent regulation of DYNC1I2 expression. Therefore, we correlated the expression of these miRNA candidates with DYNC1I2 using RNA sequencing data of 167 primary MB samples. Out of these six miRNAs, four were not detected in MB patient samples, while two miRNAs showed expression in MB patient samples, namely hsa-miR-425-5p (see binding site in Fig. 5a) and hsa-miR-1915.
Thus, our data unravel a regulatory network requiring HHIP-AS1 to bind to DYNC1I2 mRNA, to prevent DYNC1I2 depletion ig. 5 HHIP-AS1 blocks endogenous hsa-miR-425-5p function to maintain DYNC1I2 levels. a Schematic view of the complex structure generated by the predicted base-pairing interaction between HHIP-AS1 (red) and the 5'UTR of DYNC1I2 mRNA (green). The sequence of hsa-miR-425-5p is also shown and the inferred binding site of this miRNA on DYNC1I2 mRNA 5'UTR is depicted in orange. The scheme highlights how this miRNA-mRNA interaction is predicted to be blocked by HHIP-AS1 association to DYNC1I2 mRNA. Black frame indicates position for zoom out image on the right side. b The wild type 5'UTR of DYNC1I2 mRNA (5'WT) or a mutated version (5'MUT) were cloned in front of a luciferase coding sequence and co-transfected in combination with hsa-miR-425-5p mimics (mir-425) or negative controls (NC) into Daoy cells. Bar graphs indicate the measured relative luciferase activity for each combination ± SEM in three independent experiments. Student's one-sided t-test; *p < 0.05. n.s. not significant. c DYNC1I2 expression level was measured via qRT-PCR in Daoy, CHLA-266 and HHU-ATRT1 cells upon stable HHIP-AS1 knockdown using two independent stable shRNAs (sh-HHIP-AS1#1 and sh-HHIP-AS1#2), in combination with or without transient inhibition of hsa-miR-425-5p. Bar graphs are presented as the mean ± SD of three independent experiments. d Proliferation rate of Daoy and CHLA-266 with stable knockdown of HHIP-AS1 (sh-HHIP-AS1#1) measured by EdU incorporation in combination with or without transient inhibition of hsa-miR-425-5p. Bar graphs are presented as the mean ± SEM of at least ten independent experiments and corresponding controls were set to 100%. Student's two-sided t-test; ***p < 0.001; **p < 0.01; *p < 0.05; n.s. not significant. Source data and exact pvalues are provided as a "Source Data file". mediated by binding of hsa-miRNA-425-5p to its 5'UTR. Together, our correlative data analysis approach in primary MB samples combined with in silico predictions and mechanistic validation, uniformly point to a previously poorly explored regulatory function of lncRNAs which consists in binding to a specific mRNA and thereby blocking endogenous miRNA binding sites.
Loss of HHIP-AS1 extends survival in SHH-driven brain tumors in vivo. We next evaluated whether targeting HHIP-AS1 affected tumor growth in vivo. To this end, we initially utilized two well-established orthotopic brain tumor models with aberrant SHH signaling activation. Remarkably, stable knockdown of HHIP-AS1 in both orthotopically engrafted Daoy and CHLA-266 cells significantly extended the survival of recipient mice compared to corresponding isogenic controls (Daoy, p = 0.0045; CHLA-266, p = 0.0011; Fig. 6a and b). Repeated luminescence measurement indicated that the loss of HHIP-AS1 affected tumor formation in vivo consistently (Fig. S10a-c). We next aimed at validating our finding using a well-characterized patient-derived xenograft model of SHH MB. We transduced cells from the SHH-Med-1712-FH PDX model in vitro with sh-HHIP-AS1 or control shRNA prior to transplantation into the cerebella of recipient mice. Knockdown of HHIP-AS1 and corresponding DYCN1I2 reduction was confirmed in this model via qRT-PCR (Fig. S10d). Interestingly, silencing of HHIP-AS1 in injected PDX cells delayed the appearance of signs of morbidity in animals, whose mean survival was significantly extended compared to control mice (p = 0.0011; Fig. 6c). Lastly, HHIP-AS1 depletion markedly reduced cell proliferation index in Med-1712-FH PDX tumors compared to control tumors, when these samples were tested for Ki67-immunoreactivity (Fig. 6d). Accordingly, we observed more differentiated cells in tumor tissue compared to control (Fig. S11a). In line with our in vitro data, we detected no change in caspase activity (cleaved caspase) in Med-1712-FH PDX tumors ( Fig. S10e and S11b). However, we observed more DNA damage in tumor tissue compared to control (Fig. 6e) and in NSC (Fig. S9f) after HHIP-AS1 depletion. Overall, these results provide compelling evidence that HHIP-AS1 promotes tumorigenicity of SHH-driven human tumors in vivo.

Discussion
Until now the complexity of the human SHH signaling pathway and regulatory feedback loops is not fully understood. Specifically, the precise knowledge of the molecular mechanisms underlying SHH pathway pro-oncogenic activity in human cells is urgently required. To this aim, the widespread use of genetic animal models of SHH-driven malignancies has been successful at identifying and describing a large number of protein-coding genes and protein post-translational modifications implicated in regulation and downstream functions of SHH signaling in tumors [43][44][45][46] . Nevertheless, despite accounting for~70% of the transcribed genome in humans 47 , mechanistic insights into the role of lncRNAs in SHH-driven cancer biology have been gained only for a very few species. A reason for this poor consideration relies on the general low level of inter-species conservation of lncRNAs, which has inevitably hindered their discovery and functional characterization in the commonly used genetic rodent models. In our study, we employed a large comparative transcriptome analysis approach across several thousands of human normal and cancerous tissues, and identified HHIP-AS1 as a lncRNA that is specifically upregulated in SHH-driven tumors and functionally required for mediating the pro-proliferative effects of oncogenic SHH signaling. Interestingly, we found that HHIP-AS1 constitutes a downstream transcriptional target of SHH signaling, providing an explanation to its restricted and elevated expression in SHH-driven entities. Specifically, we uncovered that HHIP-AS1 transcription is initiated from a bidirectional promoter shared with the known SHH-pathway regulator HHIP. However, unlike HHIP or other key proteincoding gene of the SHH pathway, HHIP-AS1 is poorly conserved across vertebrates, showing high sequence similarity between humans and other primates, but substantially no conservation between humans and rodents. Therefore, our findings pinpoint the importance of species-specific evaluation of oncogenic signaling pathways, and emphasize how some key regulatory networks or pro-oncogenic programs may not be faithfully recapitulated by rodent tumor models, despite their generally well-accepted value to study human pathogenic processes. Furthermore, by using a combined proteomic and RNA sequencing approach, we uncovered that HHIP-AS1 acts in trans by regulating the expression of DYNC1I2, which encodes for an intermediate chain of the cytoplasmic dynein-complex 1, and not in cis by regulating HHIP 48 . We, among others, could already demonstrate the power of integrated proteogenomic approaches, showing that proteomic combined with transcriptomic profiles provides a profound insight into active oncogenic gene expression regulation in pediatric brain tumors and other cancers 16,25 . The involvement of the cytoplasmic dynein-complex 1 provides an intriguing aspect of SHH pathway regulation and function. Indeed, dynein motor complexes have been long implicated in the activity of SHH signaling. However, this association was only restricted to cytoplasmic dynein-complex 2, due to its requirement at the level of primary cilia in mediating ciliary retrograde transport 49 . By characterizing the functional interaction between HHIP-AS1 and DYNC1I2 in cancer cells and in NSC, we unraveled the existence of an additonal layer whereby SHH signaling sustains cell growth and progression. SHH signaling is already known to directly promote cell cycle progression by primarily activating the expression of MYCN and Cyclin D genes, which drive cells toward the G1/S transition 50 . By sustaining the transcription of HHIP-AS1, the SHH signaling guarantees effective spindle assembly and chromosome segregation through maintenance of appropriate levels of DYNC1I2. Interestingly, we could observe in cells with HHIP-AS1 depletion that the dysregulation of mitosis led to more DNA damage in mitotic cells. Although the DNA damage response and the mitotic cell division pathways were thought to be distinct and unrelated, it was shown in several studies that mitotic cells can experience DNA damage either endogenously due to unrepaired premitotic damage or exogenously from cancer therapies amongst other causes [51][52][53] . As we demonstrate that SHH exerts a control on cell division during the time of mitosis in cancer cells and in NSC, therapeutic limitations may exist. Targeting the SHH signaling pathway seems to provide a highly innovative therapeutic option for a broad variety of cancers. Nevertheless, as SHH pathway inhibitors seem to be safe in adults, this still remains to be shown in children treated for MB and taking into consideration our findings in NSCs. While the introduction of targeted therapies represents an exciting era in personalized medicine, compounds that target developmental signaling pathways (including SHH signaling) should be carefully evaluated for specific toxicities and their potential benefits.
Depleting cells of HHIP-AS1 or blocking its interaction with DYNC1I2 mRNA exposes the latter to hsa-miRNA-425-5pmediated degradation, thereby compromising mitotic fidelity and consequently blocking cell progression in vitro and in vivo. As the power of miRNA in regulation of mRNA has been explored elaborately, we are beginning to recognize also the complex interactions of miRNAs and lncRNAs and ensuing RNA regulatory networks. Our work provides a previously unmatched molecular understanding of lncRNA function by actively competing with a miRNA binding site on a target mRNA. We show that the lncRNA HHIP-AS1 constitutes a so far unexplored target gene of SHH signaling, which enables the pro-mitotic effects of this pathway by blocking hsa-miR-425-5p-dependent inhibition of DYNC1I2 expression. This mechanism is of fundamental interest as it describes not only a major mediator of cell progression triggered by SHH activation but also as provides a previously unknown epigenetic regulation of SHH pathway in humans. However, to comprehensively understand the role of the regulatory HHIP-AS1/DYNC1I2/miR425 axis and its potent promitotic effects, further experiments are required to elucidate the relevance in SHH-driven neurogenesis and tumorigenesis.

Methods
Animal models. For the orthotopic brain tumor models we used 8 week old female NMRI-Foxn1nu/nu that were purchased from Janvier Labs, Le Genest-Saint-Isle, France. We followed both European and national regulations for animal housing, care and experimentation (Directive 86/609). The use of animals was approved by  Table S3) using Lipofectamine RNAiMAX reagent (Thermo Fisher Scientific, #13778150) and harvested for RNA isolation after 48 and/or 72 h after transfection or were used for further analyses including EdU incorporation, colony formation assays, CTG and immunoblotting. For miRNA inhibition experiments, shRNA models of each cell line (included scramble as control, sh-HHIP-AS1#1 and sh-HHIP-AS1#2) were seeded into six-well plates. After reaching about 80% confluency, cells were transfected using Lipofectamine RNAiMAX either with the miRNA inhibitor for miR-425-5p, miR-425-3p or the negative miRNA inhibitor control (see "Oligonucleotides" in Table S3).
Overexpression. For the overexpression of DYNC1I2, GLI and HHIP-AS1 in Daoy, we used a transcriptional activation by an engineered CRISPR-Cas9 complex referred to as "SAM". Cloning of SAM sgRNA was performed as previously described 56 ; for sequence details, see "gDYNC OE", "gGLI OE" and "gHHIP-AS1 OE" in "Oligonucleotides" section in Table S3. After stable transduction of the sgRNAs, we performed a transient knockdown of HHIP-AS1 as described in section "Transient siRNA and miRNA transfection".
RNA extraction, cDNA synthesis and qRT-PCR. Total RNA was extracted using TRIzol (Thermo Fisher Scientific, #15596018) according to the manufacturer's instructions or by using the MaxWell system (Promega, #AS1340). Isolated RNA was quantified by spectrophotometry and 500 ng of RNA was retrotranscribed to total cDNA using random hexamer and oligo-dT primer mix RNA fluorescence in situ hybridization (FISH). FISH was carried out based on the Stellaris RNA FISH protocol according to the manufactory. Daoy and CHLA-266 cells were fixed in 4% formaldehyde (Merck, #100496.8350) at room temperature for 10 min. Cells were permeabilized in 70% ethanol at 4°C for 1 h followed by hybridization with FITC-labeled HHIP-AS and ATTO655-labeled DYN1I2 probes in hybridization buffer (10% formamide, 10% dextran sulfate, 2x saline sodium citrate) at 37°C overnight. Nuclei were stained with 4', 6-diamidino-2-phenylindole (DAPI). Images were acquired using a wide field fluorescence microscope Axio Observer.Z1 (Carl Zeiss Microscopy, Jena, Germany) with an ApoTome 2 (Zeiss) attachment.
Bio-Layer interferometry. Bio-Layer interferometry (BLI) has been a promising technique to study the RNA-RNA binding interaction 58 . We used the "Fortebio Blitz System" (Fortebio, Fremont, CA) to study the interaction between the proposed RNA sequences along with a negative control sequence. We used 1x RNA binding buffer (RBB) containing 10 mM Tris-HCl pH 8.0, 125 mM NaCl, 125 mM KCl & 25 mM MgCl 2 to perform all binding studies. We used a modified protocol from 58 to study the binding interaction in our system. Basically, SSA biosensor tips were hydrated in RBB for at least 10 min. Next, 200 nM of biotinylated DYNC1I2 sequence (ordered from IDTdna, see section "In silico analysis of RNA binding") was loaded for 15 min after performing an initial baseline of the unloaded sensors for 30 s in RBB. Subsequently, the loaded sensors were incubated in RBB for 1 min. The loaded sensors were incubated with 200 nM of HHIP-AS1 pos/neg sequence (ordered from IDTdna, see section "In silico analysis of RNA binding") for 5 min in the association phase, and dissociation of the bound sequences was studied for 5 min again in RBB. All the RNase free buffer components were purchased from "Thermo Fisher Scientific". The biotinylated and non-biotinylated RNA sequences were ordered from IDTdna. The Super-Streptavidin (SSA) coated biosensors were purchased from Fortebio. In all cases, secondary antibodies were species-specific: chicken anti-rabbit IgG (H + L) cross-adsorbed secondary antibody, Alexa-Fluor-488 labeled (Thermo Fisher Scientific, #A-21441) and goat anti-mouse IgG2b cross-adsorbed secondary antibody, Alexa-Fluor-594 labeled (Thermo Fisher Scientific, #A-21145).
Clonogenic, caspase 3/7 activity and cell viability assays. For the analysis of self-renewal capacity, clonogenicity was analyzed. Daoy, CHLA-266 and HHU-ATRT1 were seeded on 10 cm dishes at the appropriate density and cultured for 1-3 weeks. For transient knockdown approach, cells were transfected with siPOOL against HHIP-AS1 as described above after 24 h of seeding. Stable shRNA models of each cell line (included control, sh-HHIP-AS1#1 and sh-HHIP-AS1#2) were also seeded in 10 cm dishes at the appropriate density and cultured for 1-3 weeks. The cells were washed with PBS, fixed in 10% formaldehyde for 30 min at RT and stained in 1% crystal violet for 1 h at RT. Next, cells were washed in ddH 2 O and the number of colonies was quantified. Caspase 3/7 activity was measured using Caspase-Glo 3/7 Assay System (Promega, Walldorf, Germany, #G8091) and cell viability assessment was determined by metabolic assay, using CellTiter-Glo Luminescent Cell Viability Assay (Promega, Walldorf, Germany, #G7570) according to the manufacturer's instructions.
In silico analysis of RNA binding. The potential binding between HHIP-AS1 and DYNC1I2 mRNA was analyzed in silico using IntaRNA 59,60 and http://rtools.cbrc.jp/ bioinformatics web servers. We detected a 24 nt long sequence (CCCTTGCCTACAA CCAGACTGACA) in HHIP-AS1, which binds to the 5´UTR region of DYNC1I2. This sequence we used as a "positive binding" probe for mRNA stability assay and we designed another sequence with a similar G/C content from HHIP-AS1 that was predicted not to bind DYNC1I2, as a negative control (TTCAGCCTCCAAGGGG GCTTTTAA). Secondary structures of two RNAs forming dimers were predicted with RNACOFOLD 61 , which takes into account intra-as well as intermolecular base pairs of both sequences. Secondary structures of single sequences were predicted with RNAFOLD. All calculations were performed at T = 37°C and all used programs were from the "ViennaRNA package" v. 2.4.6 62 .
mRNA stability assay. Cells were pretreated either in the presence or absence of positive binding sequence (HHIP-AS1 bind ; see "in silico analysis of RNA binding") or "not binding" sequence (HHIP-AS1 neg ) before the addition of actinomycin D (Hycultek; 10 μg/ml final concentration), a potent inhibitor of mRNA synthesis. Afterwards, total mRNA was extracted at 0-9 h and DYNC1I2 abundance was measured by qRT-PCR.
LC-MS analysis. For mass spectrometric analysis, 15 µl peptide solution per sample was analyzed on a nano-high-performance liquid chromatography electrospray ionization mass spectrometer. The analytical system was composed of a "RSLCnano U3000 HPLC" coupled to a "Orbitrap Elite mass spectrometer via a nano-electrospray ion source" (Thermo Fisher Scientific). Injected peptides were concentrated and desalted at a flow rate of 6 µl/min on a trapping column (Acclaim PepMao C18, 2 cm × 100 µm × 3 µm particle size, 100 Å pore size, Thermo Fisher Scientific) with 0.1% TFA (v/v) for 10 min. Subsequently, peptides were separated at a constant flowrate of 300 nl/min over a 120 min gradient on an analytical column (Acclaim PepMap RSLC C18, 25 cm × 75 µm × 2 µm particle size, 100 Å pore size, Thermo Fisher Scientific) at 60°C. Separation was achieved through a gradient from 4 to 40% solvent B (solvent A: 0.1% (v/v) formic acid in water, solvent B: 0.1% (v/v) formic acid, 84% (v/v) acetonitrile in water). Afterwards, peptides were ionized at a voltage of 1.400 V and introduced into the mass spectrometer operated in positive mode. Mass spectrometry scans were recorded in profile mode in a range from 350-1700 m/z at a resolution of 60,000 while tandem mass spectra were recorded in the ion trap at normal scan rate. Tandem mass spectra were recorded with a data dependent Top 20 method and 35% normalized collision energy. Dynamic exclusion was activated with a repeat count of 1 for 45 s and only charge states 2+ and 3+ were analyzed.
Computational mass spectrometric data analysis. Proteome discoverer (version 1.4.1.14, Thermo Fisher Scientific) was applied for peptide/protein identification with mascot (version 2.4, Matrix Science) as search engine employing the UniProt database (human; including isoforms; date 2016-11-01). A false discovery rate of 1% (p ≤ 0.01) on peptide level was set as the identification threshold. Proteins were quantified with Progenesis QI for Proteomics (Version 2.0, Nonlinear Dynamics, Waters Corporation).
Statistics. Unless otherwise indicated in the figure legends, error bars represent mean ± SD or SEM of at least three independent experiments for each genotype or sample, and the used statistical test is indicated in each figure legend. Graphs were generated by using GraphPad PRISM ® Version 9 Graphpad Software.
Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability
Previously published gene expression and methylation data used in Fig. 1a-e, 3b, S1b-f, S5a, b, S7a, c-e are available under 'R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl)'. RNA sequence data that support the findings of this study have been deposited in GenBank with the #GSE140741 accession code and proteomic data have been deposited in ProteomeXchange PRIDE database with the #PXD016550 accession code. The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Proteome discoverer (version 1.4.1.14, Thermo Fisher Scientific) was applied for peptide/protein identification with mascot (version 2.4, Matrix Science) as search engine employing the UniProt database (human; including isoforms; date 2016-11-01). The raw data behind data points in figures and that support the findings of this study are available in the linked. Source data provided with this paper.